To do: April 4

Please take out your IN.

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To do: April 4

Happy or not happy?

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Unit 9 Lesson 6 - Activity

Unit 9 Lesson 6

Fill in the blank:

Machine learning is only as good as the _______________ you put into it.

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Unit 9 Lesson 6

Fill in the blank:

Machine learning is only as good as the training data you put into it.

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Unit 9 Lesson 6

When you are looking at training data, ask yourself two questions:

• Is there ___________ to accurately train a computer?
• Does this data represent all possible scenarios and users without ______?

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Unit 9 Lesson 6

When you are looking at training data, ask yourself two questions:

• Is there enough data to accurately train a computer?
• Does this data represent all possible scenarios and users without bias?

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Unit 9 Lesson 6

BIAS

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Biased data favors some things and de-prioritizes or excludes others.

Unit 9 Lesson 6

Train the computer (10 minutes)

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Code.org Unit 9 Lesson 6 Bubbles 2-4, 6,7

Write in your IN:

1) Summarize what you were asked to do in these exercises.

2) Write about two takeaways from these exercises.

DATA MC Practice #1: A software company is designing a mobile game system that should be able to recognize the faces of people who are playing the game and automatically load their profiles. Which of the following actions is most likely to reduce the possibility of bias in the system?

• Testing the system with members of the software company’s staff
• Testing the system with people of different ages, genders, and ethnicities
• Testing the system to make sure that the rules of the game are clearly explained
• Testing the system to make sure that players cannot create multiple profiles

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DATA MC Practice #1: A software company is designing a mobile game system that should be able to recognize the faces of people who are playing the game and automatically load their profiles. Which of the following actions is most likely to reduce the possibility of bias in the system?

• Testing the system with members of the software company’s staff
• Testing the system with people of different ages, genders, and ethnicities
• Testing the system to make sure that the rules of the game are clearly explained
• Testing the system to make sure that players cannot create multiple profiles

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DATA MC Practice #2: A certain social media application is popular with people across the United States. The developers of the application are updating the algorithm used by the application to introduce a new feature that allows users of the application with similar interests to connect with one another. Which of the following strategies is LEAST likely to introduce bias into the application?

• Enticing users to spend more time using the application by providing the updated algorithm for users who use the application at least ten hours per week
• Inviting a random sample of all users to try out the new algorithm and provide feedback before it is released to a wider audience
• Providing the updated algorithm only to teenage users to generate excitement about the new feature
• Testing the updated algorithm with a small number of users in the city where the developers are located so that immediate feedback can be gathered

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DATA MC Practice #2: A certain social media application is popular with people across the United States. The developers of the application are updating the algorithm used by the application to introduce a new feature that allows users of the application with similar interests to connect with one another. Which of the following strategies is LEAST likely to introduce bias into the application?

• Enticing users to spend more time using the application by providing the updated algorithm for users who use the application at least ten hours per week
• Inviting a random sample of all users to try out the new algorithm and provide feedback before it is released to a wider audience
• Providing the updated algorithm only to teenage users to generate excitement about the new feature
• Testing the updated algorithm with a small number of users in the city where the developers are located so that immediate feedback can be gathered

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Unit 9 Lesson 6 - Activity

Algorithmic Justice League founded by Joy Buolamwini

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“TECHNOLOGY SHOULD SERVE ALL OF US. NOT JUST THE PRIVILEGED FEW.”

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Unit 9 Lesson 6

Video:

Describe the problem with machine learning that Joy Buolamwini discussed in her TED talk.

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Unit 9 Lesson 6

The Algorithmic Justice League’s mission is to raise awareness about the impacts of AI, equip advocates with empirical research, build the voice and choice of the most impacted communities, and galvanize researchers, policy makers, and industry practitioners to mitigate AI harms and biases. We’re building a movement to shift the AI ecosystem towards equitable and accountable AI.

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Unit 9 Lesson 6

Streaming on NetFlix

Homework - due Tues, 8am

• Submit today's 4/3 Machine Learning class notes

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